4. De Waal, D.J. : Distributions connected with a multivariate Beta statistic.ann. Math. Statist. 41,
|
|
- Alvin Barnett
- 5 years ago
- Views:
Transcription
1 PUBLIKASIES: 1969 : 1. De Waal, D.J. : An asymptotic distribution for the determinant of a noncentral Beta statistic in multivariate analysis.south African Statistical Journal. 2, De Waal, D.J. : On the noncentral distribution of the largest canonical correlation coefficient. South African Statistical Journal. 3, De Waal, D.J. : The noncentral Beta type 2 distribution. South African Statistical Journal : 4. De Waal, D.J. : Distributions connected with a multivariate Beta statistic.ann. Math. Statist. 41, : 5. De Waal, D.J. : On the expected values of the elementary symmetric functions of a Wishart matrix. Ann. Math. Statist. 43, De Waal, D.J. : An asymptotic distribution on non-central multivariate Dirichlet variates. South African Statistical Journal. 7, : 7. De Waal, D.J. and Nel, D.G. : On some expections with respect to Wishart matrices. South African Statistical Journal. 7, De Waal, D.J. : On the ESF'S of the Wishart and correlation matrices. South African Statistical Journal. 7, Crowther, N.A.S. and de Waal, D.J. : On the distribution of a generalized positive semi definite quadratic form of normal variates. South African Statistical Journal. 7, : 10. De Waal, D.J. : Bayes estimate of the non-centrality parameter in multivariate analysis. Comm. in Statistics, 3(1), : 11. De Waal, D.J. : Bayesian inferences on the non centrality parameter of Hotellings T 2 statistic. South African Statistical Journal. 8, : 12. De Waal, D.J. : A review of tests of various hyptheses in multivariate statistical analysis. South African Statistical Journal. 10,
2 1977 : 13. De Waal, D.J. : Limiting distributions of the elementary symmetric functions of some random matrices. South African Statistical Journal. 11, De Waal, D.J. : Asymptotic distributions for the ESF'S of two matrices under the assumption of linearity. J. Multivariate Analysis, 7, : 15. De Waal, D.J. : The expected values of the elementary symmetric functions of some matrices. South African Statistical Journal. 12, Van der Merwe, A.J. and de Waal, D.J. : The asymptotic expansions of the Stein estimators for the vector case. Ann. Inst. Statistical Math. 30, A, : 17. Groenewald, P.C.N. and de Waal, D.J. : An asymptotic distribution for the coefficient in a multiple time series. South African Statistical Journal. 13, De Waal, D.J. : On the normalizing constant for the Bingham Von Mises Fisher matrix distribution. South African Statistical Journal. 13, Nagel, P.J.A. and de Waal, D.J. : Bayesian classification, estimation and prediction of growth curves. South African Statistical Journal. 13, : 20. De Waal, D.J.; van der Merwe, A.J.; Groenewald, P.C.N.; Nel, D.G. and Lombard, C.J. : Model selection, prediction and estimation for multivariate normal populations. South African Statistical Journal. 15, : 21. De Waal, D.J.; van der Merwe, A.J.; Groenewald, P.C.N. : Prediction in multivariate regression analysis. South African Statistical Journal. 16, De Waal, D.J. : Appendix in : Temporal fluctuations in the numbers of female mosquitoes trapped at a site in the Western Orange Free State. J. Ent. Soc. South Africa, 45, Rautenbach, H.F.P.; Els, D.L. and de Waal, D.J. : 'n Trosanalise as meervoudige vergelykings prosedure vir pare kontraste 'n Empiriese evaluasie en vergelyking met twee bekende prosedures. Agroplantae, 14, : 24. Van der Merwe, A.J.; Groenewald, P.C.N.; de Waal, D.J. and van der Merwe, C.A. : Model selection for future data in the case of multivariate regression analysis. South African Statistical Journal. 17,
3 25. De Waal, D.J. : Quadratic forms and manifold normal distributions. Contributions to statistics. Ed. P.K. Senl. pp (North Holland). 26. De Waal, D.J. : 'n Oorsig oor statistiese Bayesbeslissings teorie in die geval van meer as een individu. S.A. Tydskrif vir Natuurwetenskap en Tegnologie 2, (Uitnodigings artikel) 1984 : 27. De Waal, D.J.; Garisch, I. and Groenewald, P.C.N. : A super Bayesians' solution to a multi Bayesian decision problem. South African Statistical Journal. 18, Nel, D.G.; de Waal, D.J. and Marx, D.G. : A predictive approach to the detection of additional information in a multivariate regression model. Communications in Statistics (Theory and Meth.) 13, : 29. De Waal, D.J. : Matrix valued distributions. Encyclopedia of Statistical Sciences Vol. 5. Ed. Kotz and Johnson, p Van Zyl, J.M. and de Waal, D.J. : The multi Bayesian sequential decision procedure. South African Statistical Journal. 19, De Waal, D.J. and Maritz, J.S. : An empirical Bayes approach in estimating the parameters of two or more geometric distributions. Comm. in Statistics (Theory and Meth.) 14, De Waal, D.J.; Groenewald, P.C.N.; Van Zyl, J.M. and Zidek, J.V. : A Randomized solution for multi Bayes estimates of the multinormal mean. Int. Statist. Math. 37, : 33. De Waal, D.J.; Groenewald, P.C.N.; Van Zyl, J.M. and Zidek, J.V. : Multi Bayesian estimation theory. Statistics and Decisions, 4, : 34. De Waal, D.J. : Test of hypothesis in a multivariate hypergeometric case using a Bayesian approach. South African Statistical Journal. 27, : 35. De Waal, D.J. : On Bayes estimation and hypothesis testing. S.A. Journal for Science and Technology, 7, (Guest speaker at the S.A. Academy in Afrikaans). 36. De Waal, D.J. and Groenewald, P.C.N. : Bayesian tests for hypothesis of the equality of multinomial Probabilities. South African Statistical Journal. 22, De Waal, D.J. and Nel, D.G. : A procedure to select a ML II prior in a multivariate normal case. Comm. Statist. Simula., 17(3),
4 1989 : 38. De Waal, D.J. and Groenewald, P.C.N. : Bayesian tests for some precise hypotheses on multi normal means. Austrl. J. Statist., 31, No De Waal, D.J. and Groenewald, P.C.N. : A Discussion on measuring the amount of information from the data in a Bayesian analysis. South African Statistical Journal. 23, 1, De Waal, D.J. and Groenewald, P.C.N. : A Bayesian analysis of the bio availability of four brands of medicine. Australian J. of Statistics : 41. Garisch, I.; de Waal, D.J. and Groenewald, P.C.N. : The use of utilities and experts in decision making. Commun. in Statistics (Theory and Meth.), 20, No.1, Van Tonder, G.J.; Botha, J.F. and de Waal, D.J. : Bayesian estimation of waterlevels. Model Care 90 : Calibration and Reliability in Groundwater modelling. Proceedings of Conference, The Hague, IAHS. Publ. 195, : 43. Garisch, I.; de Waal, D.J. and Groenewald, P.C.N. : The use of utilities and experts in decision making. Commun. in Statistics (Theory and Meth.), 20, No.1, : 44. Steyn, P.W. and de Waal, D.J. : Occupancy distributions for testing marginal homogeinity. Commun. in Statistics (Theory Meth.), 22(5), : 45. Makhuvha, V.T.; Groenewald, P.C.N. and de Waal, D.J. : Posterior probabilities for some regression hypotheses. South African Statistical Journal., 28, No.2, p : 46. De Waal, D.J. Groenewald, P.C.N. and C.J. Kemp : Perturbation of the normal model in linear regression. South African Statistical Journal, 29, De Waal, D.J. and Groenewald, P.C.N. : Bayesian estimation of ground water levels. South African Statistical Journal., 29, p De Waal, D.J. and Kemp, C.J. : A Bayesian model for estimating the failure rate for different groups. South African Statistical Journal, 29,
5 1996 : 49. De Waal D.J. : Goodness of fit of the Generalized Extreme Value distribution based on the Kullback Leibler information. South African Statistical Journal, 30, Makhuvha, V.T., Groenewald, P.C.N. and de Waal, D.J. : Bayesian tests for the balanced two way analysis of variance model. Journal of Statistical Planning and Inference, 53, : 51. De Waal, D.J. : Number of tea bags left in the box. The Mathematical Scientist, 22 No.1, : 52. De Waal, D.J. : Posterior distribution of measures of association between predictors of horse racing results. South African Statistical Journal, 32, De Waal, D.J., Worku, Z.B. and Groenewald, P.C.N. Effect of the duration of breastfeeding on the lifetime of children in Lesotho. South African Statistical Journal, 32, De Waal, D.J. : Discussion on "Bayesian Methods in the Atmospheric Sciences" by Berliner, L.M. Royle, A., Wilke, C.K. and Milliff, R.F. In Bayesian Statistics 6. J.M. Bernardo, J.O. Berger, A.P. Dawid and A.F.M. Smith (Eds.) Oxford University Press : 55. Beirlant, J., de Waal, D.J. Teugels, J.L. A Multivariate Generalized Burr Gamma Distribution. South African Statistical Journal, Vol.34, nr : 56. De Waal, D.J., Beirlant, J. Bayesian Methods with applications to science, policy and official statistics, selected papers from ISBA 2000 : Monographs of Official Statistics George, I.E. (ed). Eurostat, : 57. Beirlant J., de Waal D.J., Teugels J.L. The generalized Burr gamma family of distributions with applications in extreme value analysis. Proceedings of the 4 th Conference on Limit Theorems in Probability and Statistics of the J. Bolyai Soc. Vol. 1,
6 2003: 58. De Waal, D.J., Van Gelder P.H.A.J.M. and Beirlant, J.: Joint modelling of daily maximum wind strengths. Paper under review for J. Wind Engineering. 59. De Waal, D.J.: Bayesian Methodology in Extreme Value Statistics. Chapter in book by Beirlant and Teugels on Extreme Values, Wiley (too appear in 2003). 2004: 60. De Waal, D.J., Van Gelder P.H.A.J.M. and Beirlant, J. (2004): Joint modelling of daily maximum wind strengths through the multivariate Burr-Gamma distribution. J. Wind Engineering and Industrial Aerodynamics 92, : 61. De Waal, D.J. Van Gelder, P.H.A.J.M. (2006): Modelling of extreme wave heights and periods through copulas. Extremes, Vol. 9, : 62. De Waal, D.J., Van Gelder, P.H.A.J.M. and A Nel (2007): Estimating joint tail probabilities on Rhine discharges through the logistic copula. Environmetrics 18: : 63. De Waal, D.J., Beirlant, J. and Dierckx, G. (2008): Predicting high quantiles through the Dirichlet Process on Extreme modeling. Accepted to SA Statist. J. 64. Dierckx G; Beirlant J and de Waal DJ (2008) : A new estimation method for Weibull-type tails based on the mean excess function. Accepted for publication in Journal of Statistical Planning and Inference.
Bayesian Confidence Intervals for the Mean of a Lognormal Distribution: A Comparison with the MOVER and Generalized Confidence Interval Procedures
Bayesian Confidence Intervals for the Mean of a Lognormal Distribution: A Comparison with the MOVER and Generalized Confidence Interval Procedures J. Harvey a,b, P.C.N. Groenewald b & A.J. van der Merwe
More informationStat 5101 Lecture Notes
Stat 5101 Lecture Notes Charles J. Geyer Copyright 1998, 1999, 2000, 2001 by Charles J. Geyer May 7, 2001 ii Stat 5101 (Geyer) Course Notes Contents 1 Random Variables and Change of Variables 1 1.1 Random
More informationPreface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of
Preface Introduction to Statistics and Data Analysis Overview: Statistical Inference, Samples, Populations, and Experimental Design The Role of Probability Sampling Procedures Collection of Data Measures
More informationPATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 2: PROBABILITY DISTRIBUTIONS
PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 2: PROBABILITY DISTRIBUTIONS Parametric Distributions Basic building blocks: Need to determine given Representation: or? Recall Curve Fitting Binary Variables
More informationPROBABILITY DISTRIBUTIONS. J. Elder CSE 6390/PSYC 6225 Computational Modeling of Visual Perception
PROBABILITY DISTRIBUTIONS Credits 2 These slides were sourced and/or modified from: Christopher Bishop, Microsoft UK Parametric Distributions 3 Basic building blocks: Need to determine given Representation:
More informationCOPYRIGHTED MATERIAL CONTENTS. Preface Preface to the First Edition
Preface Preface to the First Edition xi xiii 1 Basic Probability Theory 1 1.1 Introduction 1 1.2 Sample Spaces and Events 3 1.3 The Axioms of Probability 7 1.4 Finite Sample Spaces and Combinatorics 15
More informationINFORMATION THEORY AND STATISTICS
INFORMATION THEORY AND STATISTICS Solomon Kullback DOVER PUBLICATIONS, INC. Mineola, New York Contents 1 DEFINITION OF INFORMATION 1 Introduction 1 2 Definition 3 3 Divergence 6 4 Examples 7 5 Problems...''.
More informationSubject CS1 Actuarial Statistics 1 Core Principles
Institute of Actuaries of India Subject CS1 Actuarial Statistics 1 Core Principles For 2019 Examinations Aim The aim of the Actuarial Statistics 1 subject is to provide a grounding in mathematical and
More informationAn Introduction to Multivariate Statistical Analysis
An Introduction to Multivariate Statistical Analysis Third Edition T. W. ANDERSON Stanford University Department of Statistics Stanford, CA WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION Contents
More informationINVERTED KUMARASWAMY DISTRIBUTION: PROPERTIES AND ESTIMATION
Pak. J. Statist. 2017 Vol. 33(1), 37-61 INVERTED KUMARASWAMY DISTRIBUTION: PROPERTIES AND ESTIMATION A. M. Abd AL-Fattah, A.A. EL-Helbawy G.R. AL-Dayian Statistics Department, Faculty of Commerce, AL-Azhar
More informationAPPROXIMATING THE GENERALIZED BURR-GAMMA WITH A GENERALIZED PARETO-TYPE OF DISTRIBUTION A. VERSTER AND D.J. DE WAAL ABSTRACT
APPROXIMATING THE GENERALIZED BURR-GAMMA WITH A GENERALIZED PARETO-TYPE OF DISTRIBUTION A. VERSTER AND D.J. DE WAAL ABSTRACT In this paper the Generalized Burr-Gamma (GBG) distribution is considered to
More informationContents. Preface to Second Edition Preface to First Edition Abbreviations PART I PRINCIPLES OF STATISTICAL THINKING AND ANALYSIS 1
Contents Preface to Second Edition Preface to First Edition Abbreviations xv xvii xix PART I PRINCIPLES OF STATISTICAL THINKING AND ANALYSIS 1 1 The Role of Statistical Methods in Modern Industry and Services
More informationDefault priors and model parametrization
1 / 16 Default priors and model parametrization Nancy Reid O-Bayes09, June 6, 2009 Don Fraser, Elisabeta Marras, Grace Yun-Yi 2 / 16 Well-calibrated priors model f (y; θ), F(y; θ); log-likelihood l(θ)
More informationBayesian Confidence Intervals for the Ratio of Means of Lognormal Data with Zeros
Bayesian Confidence Intervals for the Ratio of Means of Lognormal Data with Zeros J. Harvey a,b & A.J. van der Merwe b a Centre for Statistical Consultation Department of Statistics and Actuarial Science
More informationIrr. Statistical Methods in Experimental Physics. 2nd Edition. Frederick James. World Scientific. CERN, Switzerland
Frederick James CERN, Switzerland Statistical Methods in Experimental Physics 2nd Edition r i Irr 1- r ri Ibn World Scientific NEW JERSEY LONDON SINGAPORE BEIJING SHANGHAI HONG KONG TAIPEI CHENNAI CONTENTS
More informationCurve Fitting Re-visited, Bishop1.2.5
Curve Fitting Re-visited, Bishop1.2.5 Maximum Likelihood Bishop 1.2.5 Model Likelihood differentiation p(t x, w, β) = Maximum Likelihood N N ( t n y(x n, w), β 1). (1.61) n=1 As we did in the case of the
More informationBAYESIAN INFERENCE ON MIXTURE OF GEOMETRIC WITH DEGENERATE DISTRIBUTION: ZERO INFLATED GEOMETRIC DISTRIBUTION
IJRRAS 3 () October www.arpapress.com/volumes/vol3issue/ijrras_3 5.pdf BAYESIAN INFERENCE ON MIXTURE OF GEOMETRIC WITH DEGENERATE DISTRIBUTION: ZERO INFLATED GEOMETRIC DISTRIBUTION Mayuri Pandya, Hardik
More informationCAM Ph.D. Qualifying Exam in Numerical Analysis CONTENTS
CAM Ph.D. Qualifying Exam in Numerical Analysis CONTENTS Preliminaries Round-off errors and computer arithmetic, algorithms and convergence Solutions of Equations in One Variable Bisection method, fixed-point
More informationNoninformative Priors for the Ratio of the Scale Parameters in the Inverted Exponential Distributions
Communications for Statistical Applications and Methods 03, Vol. 0, No. 5, 387 394 DOI: http://dx.doi.org/0.535/csam.03.0.5.387 Noninformative Priors for the Ratio of the Scale Parameters in the Inverted
More informationModelling Risk on Losses due to Water Spillage for Hydro Power Generation. A Verster and DJ de Waal
Modelling Risk on Losses due to Water Spillage for Hydro Power Generation. A Verster and DJ de Waal Department Mathematical Statistics and Actuarial Science University of the Free State Bloemfontein ABSTRACT
More informationHANDBOOK OF APPLICABLE MATHEMATICS
HANDBOOK OF APPLICABLE MATHEMATICS Chief Editor: Walter Ledermann Volume VI: Statistics PART A Edited by Emlyn Lloyd University of Lancaster A Wiley-Interscience Publication JOHN WILEY & SONS Chichester
More informationSome slides from Carlos Guestrin, Luke Zettlemoyer & K Gajos 2
Logistics CSE 446: Point Estimation Winter 2012 PS2 out shortly Dan Weld Some slides from Carlos Guestrin, Luke Zettlemoyer & K Gajos 2 Last Time Random variables, distributions Marginal, joint & conditional
More informationThe Mixture Approach for Simulating New Families of Bivariate Distributions with Specified Correlations
The Mixture Approach for Simulating New Families of Bivariate Distributions with Specified Correlations John R. Michael, Significance, Inc. and William R. Schucany, Southern Methodist University The mixture
More informationJoint modelling of daily maximum wind strengths through the Multivariate Burr Gamma distribution
Journal of Wind Engineering and Industrial Aerodynamics 92 (2004) 1025 1037 www.elsevier.com/locate/jweia Joint modelling of daily maximum wind strengths through the Multivariate Burr Gamma distribution
More informationInvariant HPD credible sets and MAP estimators
Bayesian Analysis (007), Number 4, pp. 681 69 Invariant HPD credible sets and MAP estimators Pierre Druilhet and Jean-Michel Marin Abstract. MAP estimators and HPD credible sets are often criticized in
More informationBayesian Regression Linear and Logistic Regression
When we want more than point estimates Bayesian Regression Linear and Logistic Regression Nicole Beckage Ordinary Least Squares Regression and Lasso Regression return only point estimates But what if we
More informationBayesian Models in Machine Learning
Bayesian Models in Machine Learning Lukáš Burget Escuela de Ciencias Informáticas 2017 Buenos Aires, July 24-29 2017 Frequentist vs. Bayesian Frequentist point of view: Probability is the frequency of
More informationBayesian Inference for the Multivariate Normal
Bayesian Inference for the Multivariate Normal Will Penny Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK. November 28, 2014 Abstract Bayesian inference for the multivariate
More informationFundamental Probability and Statistics
Fundamental Probability and Statistics "There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are
More informationMoments of the Reliability, R = P(Y<X), As a Random Variable
International Journal of Computational Engineering Research Vol, 03 Issue, 8 Moments of the Reliability, R = P(Y
More informationMonte Carlo Methods. Handbook of. University ofqueensland. Thomas Taimre. Zdravko I. Botev. Dirk P. Kroese. Universite de Montreal
Handbook of Monte Carlo Methods Dirk P. Kroese University ofqueensland Thomas Taimre University ofqueensland Zdravko I. Botev Universite de Montreal A JOHN WILEY & SONS, INC., PUBLICATION Preface Acknowledgments
More informationPrequential Analysis
Prequential Analysis Philip Dawid University of Cambridge NIPS 2008 Tutorial Forecasting 2 Context and purpose...................................................... 3 One-step Forecasts.......................................................
More informationPattern Recognition and Machine Learning
Christopher M. Bishop Pattern Recognition and Machine Learning ÖSpri inger Contents Preface Mathematical notation Contents vii xi xiii 1 Introduction 1 1.1 Example: Polynomial Curve Fitting 4 1.2 Probability
More informationImperfect Data in an Uncertain World
Imperfect Data in an Uncertain World James B. Elsner Department of Geography, Florida State University Tallahassee, Florida Corresponding author address: Dept. of Geography, Florida State University Tallahassee,
More informationStatistical and Inductive Inference by Minimum Message Length
C.S. Wallace Statistical and Inductive Inference by Minimum Message Length With 22 Figures Springer Contents Preface 1. Inductive Inference 1 1.1 Introduction 1 1.2 Inductive Inference 5 1.3 The Demise
More informationBayesian estimation of the discrepancy with misspecified parametric models
Bayesian estimation of the discrepancy with misspecified parametric models Pierpaolo De Blasi University of Torino & Collegio Carlo Alberto Bayesian Nonparametrics workshop ICERM, 17-21 September 2012
More informationIntegrated Objective Bayesian Estimation and Hypothesis Testing
Integrated Objective Bayesian Estimation and Hypothesis Testing José M. Bernardo Universitat de València, Spain jose.m.bernardo@uv.es 9th Valencia International Meeting on Bayesian Statistics Benidorm
More informationSOME ASPECTS OF MULTIVARIATE BEHRENS-FISHER PROBLEM
SOME ASPECTS OF MULTIVARIATE BEHRENS-FISHER PROBLEM Junyong Park Bimal Sinha Department of Mathematics/Statistics University of Maryland, Baltimore Abstract In this paper we discuss the well known multivariate
More informationTest Code: STA/STB (Short Answer Type) 2013 Junior Research Fellowship for Research Course in Statistics
Test Code: STA/STB (Short Answer Type) 2013 Junior Research Fellowship for Research Course in Statistics The candidates for the research course in Statistics will have to take two shortanswer type tests
More informationGaussian Models
Gaussian Models ddebarr@uw.edu 2016-04-28 Agenda Introduction Gaussian Discriminant Analysis Inference Linear Gaussian Systems The Wishart Distribution Inferring Parameters Introduction Gaussian Density
More informationOn Bayesian Inference with Conjugate Priors for Scale Mixtures of Normal Distributions
Journal of Applied Probability & Statistics Vol. 5, No. 1, xxx xxx c 2010 Dixie W Publishing Corporation, U. S. A. On Bayesian Inference with Conjugate Priors for Scale Mixtures of Normal Distributions
More informationMachine Learning Overview
Machine Learning Overview Sargur N. Srihari University at Buffalo, State University of New York USA 1 Outline 1. What is Machine Learning (ML)? 2. Types of Information Processing Problems Solved 1. Regression
More informationDynamic Matrix-Variate Graphical Models A Synopsis 1
Proc. Valencia / ISBA 8th World Meeting on Bayesian Statistics Benidorm (Alicante, Spain), June 1st 6th, 2006 Dynamic Matrix-Variate Graphical Models A Synopsis 1 Carlos M. Carvalho & Mike West ISDS, Duke
More informationG.J.B.B., VOL.5 (1) 2016:
ON THE MAXIMUM LIKELIHOOD, BAYES AND EMPIRICAL BAYES ESTIMATION FOR THE SHAPE PARAMETER, RELIABILITY AND FAILURE RATE FUNCTIONS OF KUMARASWAMY DISTRIBUTION Nadia H. Al-Noor & Sudad K. Ibraheem College
More informationWolfgang Karl Härdle Leopold Simar. Applied Multivariate. Statistical Analysis. Fourth Edition. ö Springer
Wolfgang Karl Härdle Leopold Simar Applied Multivariate Statistical Analysis Fourth Edition ö Springer Contents Part I Descriptive Techniques 1 Comparison of Batches 3 1.1 Boxplots 4 1.2 Histograms 11
More informationOn the Fisher Bingham Distribution
On the Fisher Bingham Distribution BY A. Kume and S.G Walker Institute of Mathematics, Statistics and Actuarial Science, University of Kent Canterbury, CT2 7NF,UK A.Kume@kent.ac.uk and S.G.Walker@kent.ac.uk
More informationMohsen Pourahmadi. 1. A sampling theorem for multivariate stationary processes. J. of Multivariate Analysis, Vol. 13, No. 1 (1983),
Mohsen Pourahmadi PUBLICATIONS Books and Editorial Activities: 1. Foundations of Time Series Analysis and Prediction Theory, John Wiley, 2001. 2. Computing Science and Statistics, 31, 2000, the Proceedings
More informationQuantum Minimax Theorem (Extended Abstract)
Quantum Minimax Theorem (Extended Abstract) Fuyuhiko TANAKA November 23, 2014 Quantum statistical inference is the inference on a quantum system from relatively small amount of measurement data. It covers
More informationTheory and Methods of Statistical Inference. PART I Frequentist theory and methods
PhD School in Statistics cycle XXVI, 2011 Theory and Methods of Statistical Inference PART I Frequentist theory and methods (A. Salvan, N. Sartori, L. Pace) Syllabus Some prerequisites: Empirical distribution
More informationStatistical Methods in HYDROLOGY CHARLES T. HAAN. The Iowa State University Press / Ames
Statistical Methods in HYDROLOGY CHARLES T. HAAN The Iowa State University Press / Ames Univariate BASIC Table of Contents PREFACE xiii ACKNOWLEDGEMENTS xv 1 INTRODUCTION 1 2 PROBABILITY AND PROBABILITY
More informationThe Bayesian Choice. Christian P. Robert. From Decision-Theoretic Foundations to Computational Implementation. Second Edition.
Christian P. Robert The Bayesian Choice From Decision-Theoretic Foundations to Computational Implementation Second Edition With 23 Illustrations ^Springer" Contents Preface to the Second Edition Preface
More informationMachine Learning using Bayesian Approaches
Machine Learning using Bayesian Approaches Sargur N. Srihari University at Buffalo, State University of New York 1 Outline 1. Progress in ML and PR 2. Fully Bayesian Approach 1. Probability theory Bayes
More informationInstitute of Actuaries of India
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2018 Examinations Subject CT3 Probability and Mathematical Statistics Core Technical Syllabus 1 June 2017 Aim The
More informationEstimation of parametric functions in Downton s bivariate exponential distribution
Estimation of parametric functions in Downton s bivariate exponential distribution George Iliopoulos Department of Mathematics University of the Aegean 83200 Karlovasi, Samos, Greece e-mail: geh@aegean.gr
More informationInverse Wishart Distribution and Conjugate Bayesian Analysis
Inverse Wishart Distribution and Conjugate Bayesian Analysis BS2 Statistical Inference, Lecture 14, Hilary Term 2008 March 2, 2008 Definition Testing for independence Hotelling s T 2 If W 1 W d (f 1, Σ)
More informationDirectional Statistics
Directional Statistics Kanti V. Mardia University of Leeds, UK Peter E. Jupp University of St Andrews, UK I JOHN WILEY & SONS, LTD Chichester New York Weinheim Brisbane Singapore Toronto Contents Preface
More informationDEPARTMENT OF COMPUTER SCIENCE Autumn Semester MACHINE LEARNING AND ADAPTIVE INTELLIGENCE
Data Provided: None DEPARTMENT OF COMPUTER SCIENCE Autumn Semester 203 204 MACHINE LEARNING AND ADAPTIVE INTELLIGENCE 2 hours Answer THREE of the four questions. All questions carry equal weight. Figures
More informationTheory and Methods of Statistical Inference
PhD School in Statistics cycle XXIX, 2014 Theory and Methods of Statistical Inference Instructors: B. Liseo, L. Pace, A. Salvan (course coordinator), N. Sartori, A. Tancredi, L. Ventura Syllabus Some prerequisites:
More informationTesting Statistical Hypotheses
E.L. Lehmann Joseph P. Romano Testing Statistical Hypotheses Third Edition 4y Springer Preface vii I Small-Sample Theory 1 1 The General Decision Problem 3 1.1 Statistical Inference and Statistical Decisions
More informationFoundations of Probability and Statistics
Foundations of Probability and Statistics William C. Rinaman Le Moyne College Syracuse, New York Saunders College Publishing Harcourt Brace College Publishers Fort Worth Philadelphia San Diego New York
More informationConjugate Analysis for the Linear Model
Conjugate Analysis for the Linear Model If we have good prior knowledge that can help us specify priors for β and σ 2, we can use conjugate priors. Following the procedure in Christensen, Johnson, Branscum,
More informationSTATISTICS-STAT (STAT)
Statistics-STAT (STAT) 1 STATISTICS-STAT (STAT) Courses STAT 158 Introduction to R Programming Credit: 1 (1-0-0) Programming using the R Project for the Statistical Computing. Data objects, for loops,
More informationTheory and Methods of Statistical Inference. PART I Frequentist likelihood methods
PhD School in Statistics XXV cycle, 2010 Theory and Methods of Statistical Inference PART I Frequentist likelihood methods (A. Salvan, N. Sartori, L. Pace) Syllabus Some prerequisites: Empirical distribution
More informationPattern Recognition and Machine Learning. Bishop Chapter 2: Probability Distributions
Pattern Recognition and Machine Learning Chapter 2: Probability Distributions Cécile Amblard Alex Kläser Jakob Verbeek October 11, 27 Probability Distributions: General Density Estimation: given a finite
More informationProbability for Statistics and Machine Learning
~Springer Anirban DasGupta Probability for Statistics and Machine Learning Fundamentals and Advanced Topics Contents Suggested Courses with Diffe~ent Themes........................... xix 1 Review of Univariate
More informationINTRODUCTION TO BAYESIAN INFERENCE PART 2 CHRIS BISHOP
INTRODUCTION TO BAYESIAN INFERENCE PART 2 CHRIS BISHOP Personal Healthcare Revolution Electronic health records (CFH) Personal genomics (DeCode, Navigenics, 23andMe) X-prize: first $10k human genome technology
More informationMultivariate Non-Normally Distributed Random Variables
Multivariate Non-Normally Distributed Random Variables An Introduction to the Copula Approach Workgroup seminar on climate dynamics Meteorological Institute at the University of Bonn 18 January 2008, Bonn
More informationVariable inspection plans for continuous populations with unknown short tail distributions
Variable inspection plans for continuous populations with unknown short tail distributions Wolfgang Kössler Abstract The ordinary variable inspection plans are sensitive to deviations from the normality
More informationSubjective and Objective Bayesian Statistics
Subjective and Objective Bayesian Statistics Principles, Models, and Applications Second Edition S. JAMES PRESS with contributions by SIDDHARTHA CHIB MERLISE CLYDE GEORGE WOODWORTH ALAN ZASLAVSKY \WILEY-
More informationPlausible Values for Latent Variables Using Mplus
Plausible Values for Latent Variables Using Mplus Tihomir Asparouhov and Bengt Muthén August 21, 2010 1 1 Introduction Plausible values are imputed values for latent variables. All latent variables can
More informationLearning Bayesian network : Given structure and completely observed data
Learning Bayesian network : Given structure and completely observed data Probabilistic Graphical Models Sharif University of Technology Spring 2017 Soleymani Learning problem Target: true distribution
More informationPMR Learning as Inference
Outline PMR Learning as Inference Probabilistic Modelling and Reasoning Amos Storkey Modelling 2 The Exponential Family 3 Bayesian Sets School of Informatics, University of Edinburgh Amos Storkey PMR Learning
More informationECLT 5810 Linear Regression and Logistic Regression for Classification. Prof. Wai Lam
ECLT 5810 Linear Regression and Logistic Regression for Classification Prof. Wai Lam Linear Regression Models Least Squares Input vectors is an attribute / feature / predictor (independent variable) The
More informationSCUOLA DI SPECIALIZZAZIONE IN FISICA MEDICA. Sistemi di Elaborazione dell Informazione. Regressione. Ruggero Donida Labati
SCUOLA DI SPECIALIZZAZIONE IN FISICA MEDICA Sistemi di Elaborazione dell Informazione Regressione Ruggero Donida Labati Dipartimento di Informatica via Bramante 65, 26013 Crema (CR), Italy http://homes.di.unimi.it/donida
More informationA BAYESIAN MATHEMATICAL STATISTICS PRIMER. José M. Bernardo Universitat de València, Spain
A BAYESIAN MATHEMATICAL STATISTICS PRIMER José M. Bernardo Universitat de València, Spain jose.m.bernardo@uv.es Bayesian Statistics is typically taught, if at all, after a prior exposure to frequentist
More informationPractical Bayesian Quantile Regression. Keming Yu University of Plymouth, UK
Practical Bayesian Quantile Regression Keming Yu University of Plymouth, UK (kyu@plymouth.ac.uk) A brief summary of some recent work of us (Keming Yu, Rana Moyeed and Julian Stander). Summary We develops
More informationFigure The different sources of statistical uncertainties
3.4 Uncertainties in the treatment of statistical data and influences on the structural reliability assessment * 3.4. Introduction Structural reliability methods allow us to account for the uncertain part
More informationBAYESIAN CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH GAUSSIAN PROCESS USING DIFFERENT KERNELS
BAYESIAN CLASSIFICATION OF HIGH DIMENSIONAL DATA WITH GAUSSIAN PROCESS USING DIFFERENT KERNELS Oloyede I. Department of Statistics, University of Ilorin, Ilorin, Nigeria Corresponding Author: Oloyede I.,
More informationMULTIVARIATE ANALYSIS OF VARIANCE UNDER MULTIPLICITY José A. Díaz-García. Comunicación Técnica No I-07-13/ (PE/CIMAT)
MULTIVARIATE ANALYSIS OF VARIANCE UNDER MULTIPLICITY José A. Díaz-García Comunicación Técnica No I-07-13/11-09-2007 (PE/CIMAT) Multivariate analysis of variance under multiplicity José A. Díaz-García Universidad
More informationMaximum Likelihood and Bayes Estimations under Generalized Order Statistics from Generalized Exponential Distribution
Applied Mathematical Sciences, Vol. 6, 2012, no. 49, 2431-2444 Maximum Likelihood and Bayes Estimations under Generalized Order Statistics from Generalized Exponential Distribution Saieed F. Ateya Mathematics
More informationDeciding, Estimating, Computing, Checking
Deciding, Estimating, Computing, Checking How are Bayesian posteriors used, computed and validated? Fundamentalist Bayes: The posterior is ALL knowledge you have about the state Use in decision making:
More informationDeciding, Estimating, Computing, Checking. How are Bayesian posteriors used, computed and validated?
Deciding, Estimating, Computing, Checking How are Bayesian posteriors used, computed and validated? Fundamentalist Bayes: The posterior is ALL knowledge you have about the state Use in decision making:
More informationOverall Objective Priors
Overall Objective Priors Jim Berger, Jose Bernardo and Dongchu Sun Duke University, University of Valencia and University of Missouri Recent advances in statistical inference: theory and case studies University
More informationBayesian Life Test Planning for the Weibull Distribution with Given Shape Parameter
Statistics Preprints Statistics 10-8-2002 Bayesian Life Test Planning for the Weibull Distribution with Given Shape Parameter Yao Zhang Iowa State University William Q. Meeker Iowa State University, wqmeeker@iastate.edu
More informationA Closer Look at the Hill Estimator: Edgeworth Expansions and Confidence Intervals
A Closer Look at the Hill Estimator: Edgeworth Expansions and Confidence Intervals Erich HAEUSLER University of Giessen http://www.uni-giessen.de Johan SEGERS Tilburg University http://www.center.nl EVA
More informationContents. Part I: Fundamentals of Bayesian Inference 1
Contents Preface xiii Part I: Fundamentals of Bayesian Inference 1 1 Probability and inference 3 1.1 The three steps of Bayesian data analysis 3 1.2 General notation for statistical inference 4 1.3 Bayesian
More informationA characterization of consistency of model weights given partial information in normal linear models
Statistics & Probability Letters ( ) A characterization of consistency of model weights given partial information in normal linear models Hubert Wong a;, Bertrand Clare b;1 a Department of Health Care
More informationEstimation Under Multivariate Inverse Weibull Distribution
Global Journal of Pure and Applied Mathematics. ISSN 097-768 Volume, Number 8 (07), pp. 4-4 Research India Publications http://www.ripublication.com Estimation Under Multivariate Inverse Weibull Distribution
More informationCopula based Probabilistic Measures of Uncertainty with Applications
Int. Statistical Inst.: Proc. 58th World Statistical Congress, 2011, Dublin (Session CPS057) p.5292 Copula based Probabilistic Measures of Uncertainty with Applications Kumar, Pranesh University of Northern
More informationRecent Advances in Bayesian Inference Techniques
Recent Advances in Bayesian Inference Techniques Christopher M. Bishop Microsoft Research, Cambridge, U.K. research.microsoft.com/~cmbishop SIAM Conference on Data Mining, April 2004 Abstract Bayesian
More informationBayesian Estimation of Regression Coefficients Under Extended Balanced Loss Function
Communications in Statistics Theory and Methods, 43: 4253 4264, 2014 Copyright Taylor & Francis Group, LLC ISSN: 0361-0926 print / 1532-415X online DOI: 10.1080/03610926.2012.725498 Bayesian Estimation
More informationIntroduction to the Mathematical and Statistical Foundations of Econometrics Herman J. Bierens Pennsylvania State University
Introduction to the Mathematical and Statistical Foundations of Econometrics 1 Herman J. Bierens Pennsylvania State University November 13, 2003 Revised: March 15, 2004 2 Contents Preface Chapter 1: Probability
More informationEstimators for the binomial distribution that dominate the MLE in terms of Kullback Leibler risk
Ann Inst Stat Math (0) 64:359 37 DOI 0.007/s0463-00-036-3 Estimators for the binomial distribution that dominate the MLE in terms of Kullback Leibler risk Paul Vos Qiang Wu Received: 3 June 009 / Revised:
More informationOverview of Extreme Value Theory. Dr. Sawsan Hilal space
Overview of Extreme Value Theory Dr. Sawsan Hilal space Maths Department - University of Bahrain space November 2010 Outline Part-1: Univariate Extremes Motivation Threshold Exceedances Part-2: Bivariate
More informationLinear Classification
Linear Classification Lili MOU moull12@sei.pku.edu.cn http://sei.pku.edu.cn/ moull12 23 April 2015 Outline Introduction Discriminant Functions Probabilistic Generative Models Probabilistic Discriminative
More informationvar D (B) = var(b? E D (B)) = var(b)? cov(b; D)(var(D))?1 cov(d; B) (2) Stone [14], and Hartigan [9] are among the rst to discuss the role of such ass
BAYES LINEAR ANALYSIS [This article appears in the Encyclopaedia of Statistical Sciences, Update volume 3, 1998, Wiley.] The Bayes linear approach is concerned with problems in which we want to combine
More informationEstimation of the Bivariate Generalized. Lomax Distribution Parameters. Based on Censored Samples
Int. J. Contemp. Math. Sciences, Vol. 9, 2014, no. 6, 257-267 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ijcms.2014.4329 Estimation of the Bivariate Generalized Lomax Distribution Parameters
More informationSpatial Bayesian Nonparametrics for Natural Image Segmentation
Spatial Bayesian Nonparametrics for Natural Image Segmentation Erik Sudderth Brown University Joint work with Michael Jordan University of California Soumya Ghosh Brown University Parsing Visual Scenes
More informationEnsemble Copula Coupling (ECC)
Ensemble Copula Coupling (ECC) Tilmann Gneiting Institut für Angewandte Mathematik Universität Heidelberg BfG Kolloquium, Koblenz 24. September 2013 Statistical Postprocessing of Ensemble Forecasts: EMOS/NR
More informationEstimation of Operational Risk Capital Charge under Parameter Uncertainty
Estimation of Operational Risk Capital Charge under Parameter Uncertainty Pavel V. Shevchenko Principal Research Scientist, CSIRO Mathematical and Information Sciences, Sydney, Locked Bag 17, North Ryde,
More information